Salary Prediction Classification - Project TXAI¶

Authors:

  • Martín Romero Romero
  • Valentina Isabel Ortega Pinto
In [1]:
import os

# Setting Java8 is needed for compatibility with JFML (IEEE standard 1855-2016)
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
!java -version

openjdk version "1.8.0_392"
OpenJDK Runtime Environment (build 1.8.0_392-8u392-ga-1~22.04-b08)
OpenJDK 64-Bit Server VM (build 25.392-b08, mixed mode)
In [2]:
os.system("pip install simplenlg --quiet")
os.system("pip install tabulate --quiet")
os.system("pip install interpret --quiet")
os.system("pip install xgboost --quiet")
os.system("pip install opencv-python --quiet")
os.system("pip install facets-overview --quiet")
os.system("pip install dice-ml --quiet")
os.system("pip install tensorflow --quiet")
# FAT-forenscics
os.system("pip install fat-forensics --quiet")

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
fairlearn 0.10.0 requires pandas>=2.0.3, but you have pandas 1.5.3 which is incompatible.
Out[2]:
0
In [3]:
import warnings
warnings.filterwarnings('ignore')
import sys
sys.path.append('../')
# Loading plot tool (for ploting fuzzy sets and rules)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from IPython.display import SVG, HTML, display

import base64
from facets_overview.generic_feature_statistics_generator import GenericFeatureStatisticsGenerator

# Loading csv package for reading data files
import pandas as pd

import seaborn as sns

from tabulate import tabulate

# Loading lib to support handling of confusion matrix
import numpy as np
from numpy.random.mtrand import randint

# Loading lib to support handling of iterators
import itertools

# Loading lib to deal with arff files (Weka format)
from scipy.io.arff import loadarff

# Loading some models from sklearn for performance comparison
import sklearn
from sklearn import preprocessing
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
from sklearn.tree import export_text
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer

# Loading libs for XAI comparison
from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())
from interpret.glassbox import ExplainableBoostingClassifier
from interpret import show

from numba import *
import xgboost
import shap
from xai.ExplainGAM import ExplainGAMClassifier
import graphviz

import dice_ml
from dice_ml.utils import helpers # helper functions

import cv2 as cv
from examples.common import hog
from xsvmlib.xsvmc import xSVMC
import time

# Loading the Realiser and corpus 
from simplenlg.framework import *
from simplenlg.lexicon import *
from simplenlg.realiser.english import *
from simplenlg.phrasespec import *
from simplenlg.features import *

lexicon = Lexicon.getDefaultLexicon()
nlgFactory = NLGFactory(lexicon)
realiser = Realiser(lexicon)

# Loading JFML library
from py4j.java_gateway import JavaGateway
from py4jfml.Py4Jfml import Py4jfml

# opening the JVM and Server for accessing to JFML
gateway = JavaGateway()

# Loading FCFEXPGEN library
from json import load
import re
from copy import deepcopy 
from fcfexpgen.feature import Attribute
from fcfexpgen.simple_rule import Simple_Rule
from fcfexpgen.complex_rule import Complex_Rule
from fcfexpgen.factual_explanation import Factual_Explanation
from fcfexpgen.CF_explanation import CF_Explanation

import fatf
import fatf.utils.data.datasets as fatf_datasets
import fatf.utils.data.tools as fatf_data_tools
import fatf.utils.data.density as fatf_density
import fatf.utils.models as fatf_models
import fatf.utils.metrics.tools as fatf_metrics_tools
import fatf.utils.metrics.metrics as fatf_metrics
import fatf.utils.metrics.subgroup_metrics as fatf_smt
import fatf.accountability.models.measures as fatf_accountability_models
import fatf.accountability.data.measures as fatf_accountability_data
import fatf.accountability.data.measures as fatf_dam

24-Mar-08 10:11:33 fatf.utils.array.tools INFO     Using numpy's numpy.lib.recfunctions.structured_to_unstructured as fatf.utils.array.tools.structured_to_unstructured and fatf.utils.array.tools.structured_to_unstructured_row.

Data Processing¶

In [4]:
pip install category_encoders

Requirement already satisfied: category_encoders in /opt/conda/lib/python3.11/site-packages (2.6.3)
Requirement already satisfied: numpy>=1.14.0 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (1.24.4)
Requirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (1.3.1)
Requirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (1.11.3)
Requirement already satisfied: statsmodels>=0.9.0 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (0.14.0)
Requirement already satisfied: pandas>=1.0.5 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (1.5.3)
Requirement already satisfied: patsy>=0.5.1 in /opt/conda/lib/python3.11/site-packages (from category_encoders) (0.5.3)
Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.0.5->category_encoders) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.0.5->category_encoders) (2023.3.post1)
Requirement already satisfied: six in /opt/conda/lib/python3.11/site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)
Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.11/site-packages (from scikit-learn>=0.20.0->category_encoders) (1.3.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn>=0.20.0->category_encoders) (3.2.0)
Requirement already satisfied: packaging>=21.3 in /opt/conda/lib/python3.11/site-packages (from statsmodels>=0.9.0->category_encoders) (23.2)
Note: you may need to restart the kernel to use updated packages.
In [5]:
import pandas as pd 
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import itertools
import scipy.stats as ss
from category_encoders import TargetEncoder
from sklearn.metrics import classification_report
In [6]:
csv_file_path='data/salary.csv'
df = pd.read_csv(csv_file_path)
In [7]:
df
Out[7]:
age workclass fnlwgt education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country salary
0 39 State-gov 77516 Bachelors 13 Never-married Adm-clerical Not-in-family White Male 2174 0 40 United-States <=50K
1 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 0 0 13 United-States <=50K
2 38 Private 215646 HS-grad 9 Divorced Handlers-cleaners Not-in-family White Male 0 0 40 United-States <=50K
3 53 Private 234721 11th 7 Married-civ-spouse Handlers-cleaners Husband Black Male 0 0 40 United-States <=50K
4 28 Private 338409 Bachelors 13 Married-civ-spouse Prof-specialty Wife Black Female 0 0 40 Cuba <=50K
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
32556 27 Private 257302 Assoc-acdm 12 Married-civ-spouse Tech-support Wife White Female 0 0 38 United-States <=50K
32557 40 Private 154374 HS-grad 9 Married-civ-spouse Machine-op-inspct Husband White Male 0 0 40 United-States >50K
32558 58 Private 151910 HS-grad 9 Widowed Adm-clerical Unmarried White Female 0 0 40 United-States <=50K
32559 22 Private 201490 HS-grad 9 Never-married Adm-clerical Own-child White Male 0 0 20 United-States <=50K
32560 52 Self-emp-inc 287927 HS-grad 9 Married-civ-spouse Exec-managerial Wife White Female 15024 0 40 United-States >50K

32561 rows × 15 columns

In [8]:
df = df.drop(columns=['capital-gain','capital-loss','education','fnlwgt'])
In [9]:
df['salary'] = df['salary'].str.strip()
df['salary_binary'] = df['salary'].apply(lambda x: 1 if '>50K' in x else 0)
In [10]:
df
Out[10]:
age workclass education-num marital-status occupation relationship race sex hours-per-week native-country salary salary_binary
0 39 State-gov 13 Never-married Adm-clerical Not-in-family White Male 40 United-States <=50K 0
1 50 Self-emp-not-inc 13 Married-civ-spouse Exec-managerial Husband White Male 13 United-States <=50K 0
2 38 Private 9 Divorced Handlers-cleaners Not-in-family White Male 40 United-States <=50K 0
3 53 Private 7 Married-civ-spouse Handlers-cleaners Husband Black Male 40 United-States <=50K 0
4 28 Private 13 Married-civ-spouse Prof-specialty Wife Black Female 40 Cuba <=50K 0
... ... ... ... ... ... ... ... ... ... ... ... ...
32556 27 Private 12 Married-civ-spouse Tech-support Wife White Female 38 United-States <=50K 0
32557 40 Private 9 Married-civ-spouse Machine-op-inspct Husband White Male 40 United-States >50K 1
32558 58 Private 9 Widowed Adm-clerical Unmarried White Female 40 United-States <=50K 0
32559 22 Private 9 Never-married Adm-clerical Own-child White Male 20 United-States <=50K 0
32560 52 Self-emp-inc 9 Married-civ-spouse Exec-managerial Wife White Female 40 United-States >50K 1

32561 rows × 12 columns

In [11]:
##Correalation Matrix with Numerical Data
df_with_edu = df.drop(['workclass','marital-status', 'occupation','relationship', 'race', 'sex', 'native-country','salary'], axis=1)
corr_matrix = df_with_edu.corr().abs()
# Lets plot only correlation with the class, without considering itself for better localization of the most correlated features
target_corr = corr_matrix.iloc[:-1, -1]

# Creating a df for using seaborn
target_corr_df = target_corr.to_frame().transpose()

plt.figure(figsize=(10, 10))
sns.heatmap(target_corr_df, annot=True, fmt=".2f", cmap='coolwarm', square=True, linewidths=.5, cbar_kws={"orientation": "horizontal"})
plt.title('Correlation with Numeric Data')
plt.show()
No description has been provided for this image
In [12]:
df_without_num=df.drop(['age','education-num','hours-per-week', 'salary_binary'],axis=1)
column_combinations = list(itertools.combinations(df_without_num.columns, 2))

# Inicializar un DataFrame para almacenar los coeficientes de contingencia
df_corr = pd.DataFrame(index=df_without_num.columns, columns=df_without_num.columns)

# Calcular el coeficiente de contingencia para cada par de columnas
for col1, col2 in column_combinations:
    contingency_matrix = pd.crosstab(df_without_num[col1], df_without_num[col2])
    cramers_v = ss.chi2_contingency(contingency_matrix)[0] / (sum(contingency_matrix.shape) - 1)
    df_corr.loc[col1, col2] = cramers_v
    df_corr.loc[col2, col1] = cramers_v  # La matriz es simétrica

# Normalizar los valores entre 0 y 1
df_corr = df_corr.astype(float)
df_corr = (df_corr - df_corr.min().min()) / (df_corr.max().max() - df_corr.min().min())

# Crear un mapa de colores divergentes
cmap = sns.diverging_palette(250, 10, as_cmap=True)

# Mostrar la matriz de correlación con un mapa de colores
plt.figure(figsize=(15, 15))
sns.heatmap(df_corr, annot=True, cmap=cmap, vmin=0, vmax=1, square=True)
plt.title('Contingency Coefficient Matrix (Cramer\'s V)')
plt.show()
No description has been provided for this image
In [13]:
target = df['salary_binary']
In [14]:
target
Out[14]:
0        0
1        0
2        0
3        0
4        0
        ..
32556    0
32557    1
32558    0
32559    0
32560    1
Name: salary_binary, Length: 32561, dtype: int64
In [15]:
df
Out[15]:
age workclass education-num marital-status occupation relationship race sex hours-per-week native-country salary salary_binary
0 39 State-gov 13 Never-married Adm-clerical Not-in-family White Male 40 United-States <=50K 0
1 50 Self-emp-not-inc 13 Married-civ-spouse Exec-managerial Husband White Male 13 United-States <=50K 0
2 38 Private 9 Divorced Handlers-cleaners Not-in-family White Male 40 United-States <=50K 0
3 53 Private 7 Married-civ-spouse Handlers-cleaners Husband Black Male 40 United-States <=50K 0
4 28 Private 13 Married-civ-spouse Prof-specialty Wife Black Female 40 Cuba <=50K 0
... ... ... ... ... ... ... ... ... ... ... ... ...
32556 27 Private 12 Married-civ-spouse Tech-support Wife White Female 38 United-States <=50K 0
32557 40 Private 9 Married-civ-spouse Machine-op-inspct Husband White Male 40 United-States >50K 1
32558 58 Private 9 Widowed Adm-clerical Unmarried White Female 40 United-States <=50K 0
32559 22 Private 9 Never-married Adm-clerical Own-child White Male 20 United-States <=50K 0
32560 52 Self-emp-inc 9 Married-civ-spouse Exec-managerial Wife White Female 40 United-States >50K 1

32561 rows × 12 columns

In [16]:
print(df['workclass'].nunique())
print(df['workclass'].unique())

9
[' State-gov' ' Self-emp-not-inc' ' Private' ' Federal-gov' ' Local-gov'
 ' ?' ' Self-emp-inc' ' Without-pay' ' Never-worked']
In [17]:
print(df['marital-status'].nunique())
print(df['marital-status'].unique())

7
[' Never-married' ' Married-civ-spouse' ' Divorced'
 ' Married-spouse-absent' ' Separated' ' Married-AF-spouse' ' Widowed']
In [18]:
print(df['occupation'].nunique())
print(df['occupation'].unique())

15
[' Adm-clerical' ' Exec-managerial' ' Handlers-cleaners' ' Prof-specialty'
 ' Other-service' ' Sales' ' Craft-repair' ' Transport-moving'
 ' Farming-fishing' ' Machine-op-inspct' ' Tech-support' ' ?'
 ' Protective-serv' ' Armed-Forces' ' Priv-house-serv']
In [19]:
print(df['relationship'].nunique())
print(df['relationship'].unique())

6
[' Not-in-family' ' Husband' ' Wife' ' Own-child' ' Unmarried'
 ' Other-relative']
In [20]:
print(df['race'].nunique())
print(df['race'].unique())

5
[' White' ' Black' ' Asian-Pac-Islander' ' Amer-Indian-Eskimo' ' Other']
In [21]:
print(df['sex'].nunique())
print(df['sex'].unique())

2
[' Male' ' Female']
In [22]:
print(df['native-country'].nunique())
print(df['native-country'].unique())

42
[' United-States' ' Cuba' ' Jamaica' ' India' ' ?' ' Mexico' ' South'
 ' Puerto-Rico' ' Honduras' ' England' ' Canada' ' Germany' ' Iran'
 ' Philippines' ' Italy' ' Poland' ' Columbia' ' Cambodia' ' Thailand'
 ' Ecuador' ' Laos' ' Taiwan' ' Haiti' ' Portugal' ' Dominican-Republic'
 ' El-Salvador' ' France' ' Guatemala' ' China' ' Japan' ' Yugoslavia'
 ' Peru' ' Outlying-US(Guam-USVI-etc)' ' Scotland' ' Trinadad&Tobago'
 ' Greece' ' Nicaragua' ' Vietnam' ' Hong' ' Ireland' ' Hungary'
 ' Holand-Netherlands']

Target encoding¶

In [23]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['native-country'], target)

# Transform the 'native-country' column
df['native-country_encoded'] = encoder.transform(df['native-country'])

# Drop the original 'native-country' column if needed
df.drop(columns=['native-country'], inplace=True)
In [24]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['workclass'], target)

# Transform the 'native-country' column
df['workclass_encoded'] = encoder.transform(df['workclass'])

# Drop the original 'native-country' column if needed
df.drop(columns=['workclass'], inplace=True)
In [25]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['marital-status'], target)

# Transform the 'native-country' column
df['marital-status_encoded'] = encoder.transform(df['marital-status'])

# Drop the original 'native-country' column if needed
df.drop(columns=['marital-status'], inplace=True)
In [26]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['occupation'], target)

# Transform the 'native-country' column
df['occupations_encoded'] = encoder.transform(df['occupation'])

# Drop the original 'native-country' column if needed
df.drop(columns=['occupation'], inplace=True)
In [27]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['relationship'], target)

# Transform the 'native-country' column
df['relationship_encoded'] = encoder.transform(df['relationship'])

# Drop the original 'native-country' column if needed
df.drop(columns=['relationship'], inplace=True)
In [28]:
encoder = TargetEncoder()

# Fit the encoder on the 'native-country' column
encoder.fit(df['race'], target)

# Transform the 'native-country' column
df['race_encoded'] = encoder.transform(df['race'])

# Drop the original 'native-country' column if needed
df.drop(columns=['race'], inplace=True)
In [29]:
df['sex_encoded'] = df['sex'].apply(lambda x: 1 if 'Male' in x else 0)
df.drop(columns=['sex'],inplace=True)
In [30]:
df = df.drop(columns=['salary'])
In [31]:
df
Out[31]:
age education-num hours-per-week salary_binary native-country_encoded workclass_encoded marital-status_encoded occupations_encoded relationship_encoded race_encoded sex_encoded
0 39 13 40 0 0.245835 0.271957 0.045961 0.134483 0.103070 0.25586 1
1 50 13 13 0 0.245835 0.284927 0.446848 0.484014 0.448571 0.25586 1
2 38 9 40 0 0.245835 0.218673 0.104209 0.062774 0.103070 0.25586 1
3 53 7 40 0 0.245835 0.218673 0.446848 0.062774 0.448571 0.12388 1
4 28 13 40 0 0.263146 0.218673 0.446848 0.449034 0.475128 0.12388 0
... ... ... ... ... ... ... ... ... ... ... ...
32556 27 12 38 0 0.245835 0.218673 0.446848 0.304957 0.475128 0.25586 0
32557 40 9 40 1 0.245835 0.218673 0.446848 0.124875 0.448571 0.25586 1
32558 58 9 40 0 0.245835 0.218673 0.085599 0.134483 0.063262 0.25586 0
32559 22 9 20 0 0.245835 0.218673 0.045961 0.134483 0.013220 0.25586 1
32560 52 9 40 1 0.245835 0.557348 0.446848 0.484014 0.475128 0.25586 0

32561 rows × 11 columns

In [32]:
# Get a list of column names excluding the target column
columns = [col for col in df.columns if col != 'salary_binary']

# Append the target column at the end of the list
columns.append('salary_binary')

# Reindex the DataFrame with the new column order
df = df.reindex(columns=columns)
In [33]:
df
Out[33]:
age education-num hours-per-week native-country_encoded workclass_encoded marital-status_encoded occupations_encoded relationship_encoded race_encoded sex_encoded salary_binary
0 39 13 40 0.245835 0.271957 0.045961 0.134483 0.103070 0.25586 1 0
1 50 13 13 0.245835 0.284927 0.446848 0.484014 0.448571 0.25586 1 0
2 38 9 40 0.245835 0.218673 0.104209 0.062774 0.103070 0.25586 1 0
3 53 7 40 0.245835 0.218673 0.446848 0.062774 0.448571 0.12388 1 0
4 28 13 40 0.263146 0.218673 0.446848 0.449034 0.475128 0.12388 0 0
... ... ... ... ... ... ... ... ... ... ... ...
32556 27 12 38 0.245835 0.218673 0.446848 0.304957 0.475128 0.25586 0 0
32557 40 9 40 0.245835 0.218673 0.446848 0.124875 0.448571 0.25586 1 1
32558 58 9 40 0.245835 0.218673 0.085599 0.134483 0.063262 0.25586 0 0
32559 22 9 20 0.245835 0.218673 0.045961 0.134483 0.013220 0.25586 1 0
32560 52 9 40 0.245835 0.557348 0.446848 0.484014 0.475128 0.25586 0 1

32561 rows × 11 columns

In [34]:
target
Out[34]:
0        0
1        0
2        0
3        0
4        0
        ..
32556    0
32557    1
32558    0
32559    0
32560    1
Name: salary_binary, Length: 32561, dtype: int64

Correlation Matrix for encoded data¶

In [35]:
##Correalation Matrix with Numerical Data
corr_matrix = df.corr().abs()
# Lets plot only correlation with the class, without considering itself for better localization of the most correlated features
target_corr = corr_matrix.iloc[:-1, -1]

# Creating a df for using seaborn
target_corr_df = target_corr.to_frame().transpose()

plt.figure(figsize=(15, 15))
sns.heatmap(target_corr_df, annot=True, fmt=".2f", cmap='coolwarm', square=True, linewidths=.5, cbar_kws={"orientation": "horizontal"})
plt.title('Correlation with Encoded Data')
plt.show()
No description has been provided for this image
In [36]:
df = df.drop(columns=['salary_binary'])
In [37]:
df
Out[37]:
age education-num hours-per-week native-country_encoded workclass_encoded marital-status_encoded occupations_encoded relationship_encoded race_encoded sex_encoded
0 39 13 40 0.245835 0.271957 0.045961 0.134483 0.103070 0.25586 1
1 50 13 13 0.245835 0.284927 0.446848 0.484014 0.448571 0.25586 1
2 38 9 40 0.245835 0.218673 0.104209 0.062774 0.103070 0.25586 1
3 53 7 40 0.245835 0.218673 0.446848 0.062774 0.448571 0.12388 1
4 28 13 40 0.263146 0.218673 0.446848 0.449034 0.475128 0.12388 0
... ... ... ... ... ... ... ... ... ... ...
32556 27 12 38 0.245835 0.218673 0.446848 0.304957 0.475128 0.25586 0
32557 40 9 40 0.245835 0.218673 0.446848 0.124875 0.448571 0.25586 1
32558 58 9 40 0.245835 0.218673 0.085599 0.134483 0.063262 0.25586 0
32559 22 9 20 0.245835 0.218673 0.045961 0.134483 0.013220 0.25586 1
32560 52 9 40 0.245835 0.557348 0.446848 0.484014 0.475128 0.25586 0

32561 rows × 10 columns

In [38]:
feature_names_array = df.columns.values
print(feature_names_array)

['age' 'education-num' 'hours-per-week' 'native-country_encoded'
 'workclass_encoded' 'marital-status_encoded' 'occupations_encoded'
 'relationship_encoded' 'race_encoded' 'sex_encoded']
In [39]:
target
Out[39]:
0        0
1        0
2        0
3        0
4        0
        ..
32556    0
32557    1
32558    0
32559    0
32560    1
Name: salary_binary, Length: 32561, dtype: int64

Splitting data for train and test¶

In [40]:
x_train, x_test, y_train, y_test = train_test_split(df, target,test_size=0.1, random_state=42)

Explainability¶

1. Creation of models¶

In [41]:
## this code is developed by Jose Maria Alonso-Moral
x_tr = x_train
y_tr = y_train

xgb = xgboost.XGBRegressor().fit(x_tr, y_tr)

dtc = tree.DecisionTreeClassifier()
dtc.fit(x_tr, y_tr)

dtc5 = tree.DecisionTreeClassifier(max_depth=5)
dtc5.fit(x_tr, y_tr)

gam = ExplainGAMClassifier()
gam.fit(x_tr, y_tr)

ebm = ExplainableBoostingClassifier()
ebm.fit(x_tr,y_tr)

randf = RandomForestClassifier(n_estimators=500)
randf.fit(x_tr, y_tr)

models = [ebm, gam, dtc, dtc5, randf]
models_names = ['EBM', 'GAM', 'TREE', 'TREE5', 'RF']

# %% cross-validation
print("1) cross-validation (over training data)")
scorings = ['accuracy', 'f1']  # For binary classification
i = 0
nF= 5
for mm in models:
    cv_results = cross_validate(mm, x_tr, y_tr, cv=nF, 
                            scoring=scorings,
                            return_train_score=False)
    print()
    print(models_names[i] + ':')
    i = i+1
    am1=np.mean(cv_results['test_accuracy'])
    am1s=np.std(cv_results['test_accuracy'])
    am2=np.mean(cv_results['test_f1'])
    am2s=np.std(cv_results['test_f1'])
    print('   Average Correct Classification Rate = ' + str('%0.3f' %am1))
    print('   Stdev Correct Classification Rate = ' + str('%0.3f' %am1s))
    print('   Average F-score = ' + str('%0.3f' %am2))
    print('   Stdev F-score = ' + str('%0.3f' %am2s))

# %% test with unknown data
print()
print("2) test with unknown data")

i=0
models_acc=[]
target_names = ['Lower than 50k', 'Higher than 50k']
for mm in models:
    print()
    print(models_names[i] + ':')
    i = i+1
    sc= mm.score(x_test,y_test)
    # Mean accuracy of self.predict(x_test) wrt y_test
    print("Correct Classifiction Rate: "+str('%0.3f' %sc))
    models_acc.append(round(sc,3))
    y_pred= mm.predict(x_test)
    print(classification_report(y_test, y_pred, target_names=target_names))

1) cross-validation (over training data)

EBM:
   Average Correct Classification Rate = 0.841
   Stdev Correct Classification Rate = 0.003
   Average F-score = 0.638
   Stdev F-score = 0.008

GAM:
   Average Correct Classification Rate = 0.840
   Stdev Correct Classification Rate = 0.002
   Average F-score = 0.628
   Stdev F-score = 0.006

TREE:
   Average Correct Classification Rate = 0.781
   Stdev Correct Classification Rate = 0.005
   Average F-score = 0.539
   Stdev F-score = 0.012

TREE5:
   Average Correct Classification Rate = 0.830
   Stdev Correct Classification Rate = 0.003
   Average F-score = 0.597
   Stdev F-score = 0.021

RF:
   Average Correct Classification Rate = 0.823
   Stdev Correct Classification Rate = 0.006
   Average F-score = 0.607
   Stdev F-score = 0.012

2) test with unknown data

EBM:
Correct Classifiction Rate: 0.847
                 precision    recall  f1-score   support

 Lower than 50k       0.87      0.93      0.90      2456
Higher than 50k       0.74      0.58      0.65       801

       accuracy                           0.85      3257
      macro avg       0.81      0.76      0.78      3257
   weighted avg       0.84      0.85      0.84      3257


GAM:
Correct Classifiction Rate: 0.845
                 precision    recall  f1-score   support

 Lower than 50k       0.87      0.93      0.90      2456
Higher than 50k       0.74      0.57      0.64       801

       accuracy                           0.84      3257
      macro avg       0.80      0.75      0.77      3257
   weighted avg       0.84      0.84      0.84      3257


TREE:
Correct Classifiction Rate: 0.784
                 precision    recall  f1-score   support

 Lower than 50k       0.85      0.86      0.86      2456
Higher than 50k       0.56      0.54      0.55       801

       accuracy                           0.78      3257
      macro avg       0.71      0.70      0.70      3257
   weighted avg       0.78      0.78      0.78      3257


TREE5:
Correct Classifiction Rate: 0.833
                 precision    recall  f1-score   support

 Lower than 50k       0.85      0.95      0.90      2456
Higher than 50k       0.75      0.49      0.59       801

       accuracy                           0.83      3257
      macro avg       0.80      0.72      0.74      3257
   weighted avg       0.82      0.83      0.82      3257


RF:
Correct Classifiction Rate: 0.827
                 precision    recall  f1-score   support

 Lower than 50k       0.87      0.90      0.89      2456
Higher than 50k       0.67      0.59      0.63       801

       accuracy                           0.83      3257
      macro avg       0.77      0.75      0.76      3257
   weighted avg       0.82      0.83      0.82      3257

2. InterpretML for EBM¶

In [42]:
# explanations provided by interpretML
#ebm.fit(x_tr, y_tr) # execute this only once

ebm_global = ebm.explain_global()
show(ebm_global)
In [43]:
salary_ind = [1,2]
for i in salary_ind:
    idx_instance = i  # Explain the classification with instance (x[idx])
    ebm_local = ebm.explain_local(x_tr.iloc[idx_instance-1 : idx_instance],y_tr.iloc[idx_instance-1 : idx_instance])
    show(ebm_local)

3. SHAP Values for XGB¶

In [44]:
# Explaining with SHAP
explainer = shap.Explainer(xgb)
shap_values = explainer(x_tr)

# visualize the global prediction's explanation
shap.plots.bar(shap_values)
No description has been provided for this image
In [45]:
# visualizing SHAP explanations for single instances
for i in salary_ind:
  shap.plots.waterfall(shap_values[i])
No description has been provided for this image
No description has been provided for this image

4. Visualizing trees¶

In [46]:
# Visualizing entire decision tree
print(export_text(dtc5, feature_names=feature_names_array))
dot_data = tree.export_graphviz(dtc5, out_file=None, filled=True, rounded=True, special_characters=True, feature_names=feature_names_array) 
graph = graphviz.Source(dot_data) 
graph

|--- relationship_encoded <= 0.28
|   |--- education-num <= 13.50
|   |   |--- hours-per-week <= 42.50
|   |   |   |--- occupations_encoded <= 0.29
|   |   |   |   |--- education-num <= 12.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- education-num >  12.50
|   |   |   |   |   |--- class: 0
|   |   |   |--- occupations_encoded >  0.29
|   |   |   |   |--- age <= 34.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  34.50
|   |   |   |   |   |--- class: 0
|   |   |--- hours-per-week >  42.50
|   |   |   |--- education-num <= 12.50
|   |   |   |   |--- age <= 38.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  38.50
|   |   |   |   |   |--- class: 0
|   |   |   |--- education-num >  12.50
|   |   |   |   |--- age <= 27.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  27.50
|   |   |   |   |   |--- class: 0
|   |--- education-num >  13.50
|   |   |--- hours-per-week <= 43.50
|   |   |   |--- education-num <= 14.50
|   |   |   |   |--- age <= 47.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  47.50
|   |   |   |   |   |--- class: 0
|   |   |   |--- education-num >  14.50
|   |   |   |   |--- age <= 31.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  31.50
|   |   |   |   |   |--- class: 0
|   |   |--- hours-per-week >  43.50
|   |   |   |--- age <= 30.50
|   |   |   |   |--- marital-status_encoded <= 0.06
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- marital-status_encoded >  0.06
|   |   |   |   |   |--- class: 1
|   |   |   |--- age >  30.50
|   |   |   |   |--- occupations_encoded <= 0.47
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- occupations_encoded >  0.47
|   |   |   |   |   |--- class: 1
|--- relationship_encoded >  0.28
|   |--- education-num <= 12.50
|   |   |--- occupations_encoded <= 0.25
|   |   |   |--- education-num <= 8.50
|   |   |   |   |--- occupations_encoded <= 0.17
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- occupations_encoded >  0.17
|   |   |   |   |   |--- class: 0
|   |   |   |--- education-num >  8.50
|   |   |   |   |--- age <= 35.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  35.50
|   |   |   |   |   |--- class: 0
|   |   |--- occupations_encoded >  0.25
|   |   |   |--- age <= 32.50
|   |   |   |   |--- age <= 26.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  26.50
|   |   |   |   |   |--- class: 0
|   |   |   |--- age >  32.50
|   |   |   |   |--- education-num <= 9.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- education-num >  9.50
|   |   |   |   |   |--- class: 1
|   |--- education-num >  12.50
|   |   |--- occupations_encoded <= 0.25
|   |   |   |--- hours-per-week <= 41.50
|   |   |   |   |--- occupations_encoded <= 0.08
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- occupations_encoded >  0.08
|   |   |   |   |   |--- class: 0
|   |   |   |--- hours-per-week >  41.50
|   |   |   |   |--- workclass_encoded <= 0.28
|   |   |   |   |   |--- class: 1
|   |   |   |   |--- workclass_encoded >  0.28
|   |   |   |   |   |--- class: 0
|   |   |--- occupations_encoded >  0.25
|   |   |   |--- age <= 28.50
|   |   |   |   |--- age <= 25.50
|   |   |   |   |   |--- class: 0
|   |   |   |   |--- age >  25.50
|   |   |   |   |   |--- class: 1
|   |   |   |--- age >  28.50
|   |   |   |   |--- hours-per-week <= 31.00
|   |   |   |   |   |--- class: 1
|   |   |   |   |--- hours-per-week >  31.00
|   |   |   |   |   |--- class: 1
Out[46]:
No description has been provided for this image

5. Explanations for GAM model¶

In [47]:
# %% Plot global factual explanation
gam.explain_global_importances(x_tr,  
                               figsize=(20, 5))

# %% Plot global counterfactual explanation
gam.plot_counterfactual_importance(x_tr,  
                                   weighted=True, how_many_plots=2,  
                                   figsize=(21, 7)
                                   )
No description has been provided for this image 
100%|███████████████████████████████████████████| 10/10 [06:44<00:00, 40.44s/it]
No description has been provided for this image

6. Accuracy vs Explainability Tradeoff¶

In [48]:
def plot_pareto_front(x,y,n,labelx,labely,minx,maxx):
  plt.title("Pareto Front")
  plt.ylabel(labelx)
  plt.xlabel(labely)
  plt.axis([minx, maxx, 0, 1])
  #plt.plot(x, y, 'ro')
  c=["ro","bo","go","rs","bs","gs","r*","b*","g*","r+","b+","g+"]
  for m in n:
    m_idx= n.index(m)
    plt.plot(x[m_idx], y[m_idx], c[m_idx], label=m)

    #plt.annotate(m, xy=(x[m_idx], y[m_idx]), xytext=(x[m_idx]+0.1, y[m_idx]-0.1), arrowprops=dict(facecolor='black', shrink=0.05), )
  plt.grid(True)
  plt.legend()
  plt.show()
In [49]:
# Building and visualizing the Pareto front with Accuracy versus Number of leaves
lim= 175
m_int=[dtc.get_n_leaves(), dtc5.get_n_leaves(), lim] 
# we set RF in the limit (lim) because it is a black-box model (no easy fair way to compute its complexity in terms of rules/leaves)
# dtc is the full tree
# dtc5 has max tree depth equals 5

m_acc=[models_acc[2], models_acc[3], models_acc[4]]
m_nam= [models_names[2], models_names[3], models_names[4]]
print(m_int)
print(m_acc)
print(m_nam)
plt.figure(figsize=[15,10])
plot_pareto_front(m_int,m_acc,m_nam,'Accuracy (Classification Ratio)','Interpretability (Num of rules/leaves)',20,lim)

[6213, 32, 175]
[0.784, 0.833, 0.827]
['TREE', 'TREE5', 'RF']
No description has been provided for this image
In [50]:
def get_shap_explanation_length(single_lower_triangular_interactions, indexes=None, th=0.9):
    # Calculate the cumulative sum and absolute value using numpy functions
    shap_cumsum = np.cumsum(np.abs(single_lower_triangular_interactions))
    # Normalize the cumulative sum
    normalised_shap_cumsum = shap_cumsum / shap_cumsum[-1]
    # Find the index of the first element that exceeds the threshold using a loop
    first_above_idx = 0
    for i, val in enumerate(normalised_shap_cumsum):
        if val > th:
            first_above_idx = i
            break
    # Calculate the SHAP explanation length
    shap_expl_length = first_above_idx
    return shap_expl_length
In [51]:
SL=[]
explainerTree = shap.TreeExplainer(dtc)
shap_values_DTC = explainerTree.shap_values(x_test)
SL.append(get_shap_explanation_length(shap_values_DTC[0]))
explainerTree5 = shap.TreeExplainer(dtc5)
shap_values_DTC5 = explainerTree5.shap_values(x_test)
SL.append(get_shap_explanation_length(shap_values_DTC5[0]))
explainerTreeRF = shap.TreeExplainer(randf)
shap_values_RF = explainerTreeRF.shap_values(x_test)
SL.append(get_shap_explanation_length(shap_values_RF[0]))
print(SL)

[29360, 29360, 29341]
In [52]:
m_int=SL
m_acc=[models_acc[2], models_acc[3], models_acc[4]]
m_nam= [models_names[2], models_names[3], models_names[4]]
print(SL)
print(m_acc)
print(m_nam)
plt.figure(figsize=[15,10])
plot_pareto_front(m_int,m_acc,m_nam,'Accuracy (Classification Ratio)','Interpretability (Shap Length)',550,565)

[29360, 29360, 29341]
[0.784, 0.833, 0.827]
['TREE', 'TREE5', 'RF']
No description has been provided for this image

Finding bias¶

In [53]:
df
Out[53]:
age education-num hours-per-week native-country_encoded workclass_encoded marital-status_encoded occupations_encoded relationship_encoded race_encoded sex_encoded
0 39 13 40 0.245835 0.271957 0.045961 0.134483 0.103070 0.25586 1
1 50 13 13 0.245835 0.284927 0.446848 0.484014 0.448571 0.25586 1
2 38 9 40 0.245835 0.218673 0.104209 0.062774 0.103070 0.25586 1
3 53 7 40 0.245835 0.218673 0.446848 0.062774 0.448571 0.12388 1
4 28 13 40 0.263146 0.218673 0.446848 0.449034 0.475128 0.12388 0
... ... ... ... ... ... ... ... ... ... ...
32556 27 12 38 0.245835 0.218673 0.446848 0.304957 0.475128 0.25586 0
32557 40 9 40 0.245835 0.218673 0.446848 0.124875 0.448571 0.25586 1
32558 58 9 40 0.245835 0.218673 0.085599 0.134483 0.063262 0.25586 0
32559 22 9 20 0.245835 0.218673 0.045961 0.134483 0.013220 0.25586 1
32560 52 9 40 0.245835 0.557348 0.446848 0.484014 0.475128 0.25586 0

32561 rows × 10 columns

In [54]:
df_X = df
df_y = target.astype(int)
df_feature_names = df_X.columns.tolist()
df_class_names = target.unique()
df_X=df_X.to_numpy()
df_y=df_y.to_numpy()
df_feature_names=np.array(df_feature_names)
df_class_names=np.array(df_class_names)
print(df_X)
print(df_feature_names)
print(df_class_names)

[[3.90000000e+01 1.30000000e+01 4.00000000e+01 ... 1.03070439e-01
  2.55859937e-01 1.00000000e+00]
 [5.00000000e+01 1.30000000e+01 1.30000000e+01 ... 4.48571212e-01
  2.55859937e-01 1.00000000e+00]
 [3.80000000e+01 9.00000000e+00 4.00000000e+01 ... 1.03070439e-01
  2.55859937e-01 1.00000000e+00]
 ...
 [5.80000000e+01 9.00000000e+00 4.00000000e+01 ... 6.32617528e-02
  2.55859937e-01 0.00000000e+00]
 [2.20000000e+01 9.00000000e+00 2.00000000e+01 ... 1.32202052e-02
  2.55859937e-01 1.00000000e+00]
 [5.20000000e+01 9.00000000e+00 4.00000000e+01 ... 4.75127551e-01
  2.55859937e-01 0.00000000e+00]]
['age' 'education-num' 'hours-per-week' 'native-country_encoded'
 'workclass_encoded' 'marital-status_encoded' 'occupations_encoded'
 'relationship_encoded' 'race_encoded' 'sex_encoded']
[0 1]

Sampling bias and systematic performance bias with race¶

In [55]:
print(df['race_encoded'].max())
print(df['race_encoded'].min())
print(df['race_encoded'].unique())

0.26564003849855633
0.09225092251109193
[0.25585994 0.12387964 0.26564004 0.11575563 0.09225092]
In [56]:
# Select a feature for which the Sampling Bias be measured
selected_feature_index = 8
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [0.10, 0.12,0.14,0.26]
In [57]:
selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
selected_feature_grouping[1]
Out[57]:
['x <= 0.1',
 '0.1 < x <= 0.12',
 '0.12 < x <= 0.14',
 '0.14 < x <= 0.26',
 '0.26 < x']
In [58]:
print(len(selected_feature_grouping[0][0]))
print(len(selected_feature_grouping[0][1]))
print(len(selected_feature_grouping[0][2]))
print(len(selected_feature_grouping[0][3]))
print(len(selected_feature_grouping[0][4]))

271
311
3124
27816
1039
In [59]:
counts_per_grouping = [len(i) for i in selected_feature_grouping[0]]
print(counts_per_grouping)
print(counts_per_grouping)
fatf_accountability_data.sampling_bias_grid_check(counts_per_grouping)

[271, 311, 3124, 27816, 1039]
[271, 311, 3124, 27816, 1039]
Out[59]:
array([[False, False,  True,  True,  True],
       [False, False,  True,  True,  True],
       [ True,  True, False,  True,  True],
       [ True,  True,  True, False,  True],
       [ True,  True,  True,  True, False]])
In [60]:
# Get the disparity grid
bias_grid = fatf_dam.sampling_bias_grid_check(counts_per_grouping)
print(bias_grid)

# Print out disparity per every grouping pair
print('\nThe Sampling Bias for *{}* feature (index {}) grouping is:'
      ''.format(selected_feature_name, selected_feature_index))
for grouping_i, grouping_name_i in enumerate(counts_per_grouping):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(counts_per_grouping[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Sampling Bias.'
              ''.format(grouping_name_i, grouping_name_j, is_not))

[[False False  True  True  True]
 [False False  True  True  True]
 [ True  True False  True  True]
 [ True  True  True False  True]
 [ True  True  True  True False]]

The Sampling Bias for *race_encoded* feature (index 8) grouping is:
    * For "271" and "311" groupings there is NO Sampling Bias.
    * For "271" and "3124" groupings there is Sampling Bias.
    * For "271" and "27816" groupings there is Sampling Bias.
    * For "271" and "1039" groupings there is Sampling Bias.
    * For "311" and "3124" groupings there is Sampling Bias.
    * For "311" and "27816" groupings there is Sampling Bias.
    * For "311" and "1039" groupings there is Sampling Bias.
    * For "3124" and "27816" groupings there is Sampling Bias.
    * For "3124" and "1039" groupings there is Sampling Bias.
    * For "27816" and "1039" groupings there is Sampling Bias.
In [61]:
dtc5 = tree.DecisionTreeClassifier(max_depth=5)
dtc5.fit(df_X, df_y)
df_pred = dtc5.predict(df_X)
In [62]:
grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())
In [63]:
print('First subgroup')
print('Targets: ', df_y[selected_feature_grouping[0][0]])
print('Unique targets: ', np.unique(df_y[selected_feature_grouping[0][0]]))
print('Unique predictions: ', np.unique(df_pred[selected_feature_grouping[0][0]]))
print('Confusion matrix: ')
print(grouping_cm[0])

First subgroup
Targets:  [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1
 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 1
 0 0 0 1 0 0 0 0 0 0 0 0]
Unique targets:  [0 1]
Unique predictions:  [0 1]
Confusion matrix: 
[[236  15]
 [ 10  10]]
In [64]:
print('Second subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][1]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][1]]))
print('Confusion matrix: ')
print(grouping_cm[1])

print('Third subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][2]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][2]]))
print('Confusion matrix: ')
print(grouping_cm[2])

print('Fourth subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][2]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][2]]))
print('Confusion matrix: ')
print(grouping_cm[3])

print('Fifth subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][2]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][2]]))
print('Confusion matrix: ')
print(grouping_cm[4])

Second subgroup
Confusion matrix: 
[[265  26]
 [ 10  10]]
Third subgroup
Confusion matrix: 
[[2677  251]
 [  60  136]]
Fourth subgroup
Confusion matrix: 
[[19411  3541]
 [ 1288  3576]]
Fifth subgroup
Confusion matrix: 
[[687 131]
 [ 76 145]]
In [65]:
group_0_acc = fatf_metrics.accuracy(grouping_cm[0])
print(group_0_acc)
group_1_acc = fatf_metrics.accuracy(grouping_cm[1])
print(group_1_acc)
group_2_acc = fatf_metrics.accuracy(grouping_cm[2])
print(group_2_acc)
group_3_acc = fatf_metrics.accuracy(grouping_cm[3])
print(group_3_acc)
group_4_acc = fatf_metrics.accuracy(grouping_cm[4])
print(group_4_acc)

0.9077490774907749
0.8842443729903537
0.9004481434058899
0.8263948806442335
0.8007699711260827
In [66]:
fatf_accountability_models.systematic_performance_bias_grid([group_0_acc, group_1_acc, group_2_acc,group_3_acc,group_4_acc])
Out[66]:
array([[False, False, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False, False]])
In [67]:
# Select a predictive performance metric
predictive_performance_metric = 'accuracy'
#predictive_performance_metric = 'positive predictive value' # Notice a class index needs to be selected using "label_index" property
#predictive_performance_metric = 'false negative rate' # Notice a class index needs to be selected using "label_index" property

# Select a feature for which the difference in performance should be measured
selected_feature_index = 8 # before was 2, in FAT-forensics example was 1
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [0.10, 0.12,0.14,0.26] # before was [2.5, 4.75], , in FAT-forensics example was [3]

selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
print('Grouping using variable', df_feature_names[selected_feature_index], ' as ', selected_feature_grouping[1])

grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())

print('Resulting confusion matrices:')
for i in range(len(grouping_cm)):
    print('Confusion matrix', i, ':')
    print(grouping_cm[i])

# Compute performance per group in the feature
population_metrics, population_names = fatf_smt.performance_per_subgroup(
    df_X,
    df_y,
    df_pred,
    selected_feature_index,
    groupings=selected_feature_groups,
    metric=predictive_performance_metric, 
    label_index = 2)

#print(population_metrics)

# Print out performance per grouping
print('The *{}* for groups defined on "{}" feature (feature index '
      '{}):'.format(predictive_performance_metric, selected_feature_name,
                    selected_feature_index))
for p_name, p_metric in zip(population_names, population_metrics):
    print('    * For the population split *{}* the {} is: '
          '{:.2f}.'.format(p_name, predictive_performance_metric, p_metric))

# Evaluate Systematic Performance Bias
bias_grid = fatf_accountability_models.systematic_performance_bias_grid(population_metrics)
print('Systematic performance grid check:')
print(bias_grid)

# Print out Systematic Performance Bias for each grouping pair
print('\nThe *{}-based* Systematic Performance Bias for *{}* feature (index '
      '{}) grouping is:'.format(predictive_performance_metric,
                                selected_feature_name, selected_feature_index))
                                
for grouping_i, grouping_name_i in enumerate(population_names):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(population_names[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Systematic '
              'Performance Bias.'.format(grouping_name_i, grouping_name_j,
                                         is_not))

Grouping using variable race_encoded  as  ['x <= 0.1', '0.1 < x <= 0.12', '0.12 < x <= 0.14', '0.14 < x <= 0.26', '0.26 < x']
Resulting confusion matrices:
Confusion matrix 0 :
[[236  15]
 [ 10  10]]
Confusion matrix 1 :
[[265  26]
 [ 10  10]]
Confusion matrix 2 :
[[2677  251]
 [  60  136]]
Confusion matrix 3 :
[[19411  3541]
 [ 1288  3576]]
Confusion matrix 4 :
[[687 131]
 [ 76 145]]
The *accuracy* for groups defined on "race_encoded" feature (feature index 8):
    * For the population split *x <= 0.1* the accuracy is: 0.91.
    * For the population split *0.1 < x <= 0.12* the accuracy is: 0.88.
    * For the population split *0.12 < x <= 0.14* the accuracy is: 0.90.
    * For the population split *0.14 < x <= 0.26* the accuracy is: 0.83.
    * For the population split *0.26 < x* the accuracy is: 0.80.
Systematic performance grid check:
[[False False False False False]
 [False False False False False]
 [False False False False False]
 [False False False False False]
 [False False False False False]]

The *accuracy-based* Systematic Performance Bias for *race_encoded* feature (index 8) grouping is:
    * For "x <= 0.1" and "0.1 < x <= 0.12" groupings there is NO Systematic Performance Bias.
    * For "x <= 0.1" and "0.12 < x <= 0.14" groupings there is NO Systematic Performance Bias.
    * For "x <= 0.1" and "0.14 < x <= 0.26" groupings there is NO Systematic Performance Bias.
    * For "x <= 0.1" and "0.26 < x" groupings there is NO Systematic Performance Bias.
    * For "0.1 < x <= 0.12" and "0.12 < x <= 0.14" groupings there is NO Systematic Performance Bias.
    * For "0.1 < x <= 0.12" and "0.14 < x <= 0.26" groupings there is NO Systematic Performance Bias.
    * For "0.1 < x <= 0.12" and "0.26 < x" groupings there is NO Systematic Performance Bias.
    * For "0.12 < x <= 0.14" and "0.14 < x <= 0.26" groupings there is NO Systematic Performance Bias.
    * For "0.12 < x <= 0.14" and "0.26 < x" groupings there is NO Systematic Performance Bias.
    * For "0.14 < x <= 0.26" and "0.26 < x" groupings there is NO Systematic Performance Bias.

Sampling bias and systematic performance bias with education¶

In [68]:
print(df['education-num'].max())
print(df['education-num'].min())
print(df['education-num'].unique())

16
1
[13  9  7 14  5 10 12 11  4 16 15  3  6  2  1  8]
In [69]:
# Select a feature for which the Sampling Bias be measured
selected_feature_index = 1
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5]
In [70]:
selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
selected_feature_grouping[1]
Out[70]:
['x <= 1.5',
 '1.5 < x <= 2.5',
 '2.5 < x <= 3.5',
 '3.5 < x <= 4.5',
 '4.5 < x <= 5.5',
 '5.5 < x <= 6.5',
 '6.5 < x <= 7.5',
 '7.5 < x <= 8.5',
 '8.5 < x <= 9.5',
 '9.5 < x <= 10.5',
 '10.5 < x <= 11.5',
 '11.5 < x <= 12.5',
 '12.5 < x <= 13.5',
 '13.5 < x <= 14.5',
 '14.5 < x <= 15.5',
 '15.5 < x <= 16.5',
 '16.5 < x']
In [71]:
for i in range(len(selected_feature_groups)):
    print(len(selected_feature_grouping[0][i]))

51
168
333
646
514
933
1175
433
10501
7291
1382
1067
5355
1723
576
413
In [72]:
counts_per_grouping = [len(i) for i in selected_feature_grouping[0]]
print(counts_per_grouping)
print(counts_per_grouping)
fatf_accountability_data.sampling_bias_grid_check(counts_per_grouping)

[51, 168, 333, 646, 514, 933, 1175, 433, 10501, 7291, 1382, 1067, 5355, 1723, 576, 413, 0]
[51, 168, 333, 646, 514, 933, 1175, 433, 10501, 7291, 1382, 1067, 5355, 1723, 576, 413, 0]
Out[72]:
array([[False,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True,  True],
       [ True, False,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True,  True],
       [ True,  True, False,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True,  True],
       [ True,  True,  True, False,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True, False,  True,  True],
       [ True,  True,  True,  True, False,  True,  True, False,  True,
         True,  True,  True,  True,  True, False,  True,  True],
       [ True,  True,  True,  True,  True, False,  True,  True,  True,
         True,  True, False,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True, False,  True,  True,
         True, False, False,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True, False,  True,  True, False,  True,
         True,  True,  True,  True,  True,  True, False,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True, False,
         True,  True,  True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
        False,  True,  True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True, False,  True,  True,
         True, False,  True,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True, False, False,  True,  True,
         True,  True, False,  True,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True, False,  True,  True,  True,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True, False,  True,  True,  True],
       [ True,  True,  True, False, False,  True,  True,  True,  True,
         True,  True,  True,  True,  True, False,  True,  True],
       [ True,  True,  True,  True,  True,  True,  True, False,  True,
         True,  True,  True,  True,  True,  True, False,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True, False]])
In [73]:
# Get the disparity grid
bias_grid = fatf_dam.sampling_bias_grid_check(counts_per_grouping)
print(bias_grid)

# Print out disparity per every grouping pair
print('\nThe Sampling Bias for *{}* feature (index {}) grouping is:'
      ''.format(selected_feature_name, selected_feature_index))
for grouping_i, grouping_name_i in enumerate(counts_per_grouping):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(counts_per_grouping[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Sampling Bias.'
              ''.format(grouping_name_i, grouping_name_j, is_not))

[[False  True  True  True  True  True  True  True  True  True  True  True
   True  True  True  True  True]
 [ True False  True  True  True  True  True  True  True  True  True  True
   True  True  True  True  True]
 [ True  True False  True  True  True  True  True  True  True  True  True
   True  True  True  True  True]
 [ True  True  True False  True  True  True  True  True  True  True  True
   True  True False  True  True]
 [ True  True  True  True False  True  True False  True  True  True  True
   True  True False  True  True]
 [ True  True  True  True  True False  True  True  True  True  True False
   True  True  True  True  True]
 [ True  True  True  True  True  True False  True  True  True False False
   True  True  True  True  True]
 [ True  True  True  True False  True  True False  True  True  True  True
   True  True  True False  True]
 [ True  True  True  True  True  True  True  True False  True  True  True
   True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True False  True  True
   True  True  True  True  True]
 [ True  True  True  True  True  True False  True  True  True False  True
   True  True  True  True  True]
 [ True  True  True  True  True False False  True  True  True  True False
   True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True  True  True
  False  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True  True  True
   True False  True  True  True]
 [ True  True  True False False  True  True  True  True  True  True  True
   True  True False  True  True]
 [ True  True  True  True  True  True  True False  True  True  True  True
   True  True  True False  True]
 [ True  True  True  True  True  True  True  True  True  True  True  True
   True  True  True  True False]]

The Sampling Bias for *education-num* feature (index 1) grouping is:
    * For "51" and "168" groupings there is Sampling Bias.
    * For "51" and "333" groupings there is Sampling Bias.
    * For "51" and "646" groupings there is Sampling Bias.
    * For "51" and "514" groupings there is Sampling Bias.
    * For "51" and "933" groupings there is Sampling Bias.
    * For "51" and "1175" groupings there is Sampling Bias.
    * For "51" and "433" groupings there is Sampling Bias.
    * For "51" and "10501" groupings there is Sampling Bias.
    * For "51" and "7291" groupings there is Sampling Bias.
    * For "51" and "1382" groupings there is Sampling Bias.
    * For "51" and "1067" groupings there is Sampling Bias.
    * For "51" and "5355" groupings there is Sampling Bias.
    * For "51" and "1723" groupings there is Sampling Bias.
    * For "51" and "576" groupings there is Sampling Bias.
    * For "51" and "413" groupings there is Sampling Bias.
    * For "51" and "0" groupings there is Sampling Bias.
    * For "168" and "333" groupings there is Sampling Bias.
    * For "168" and "646" groupings there is Sampling Bias.
    * For "168" and "514" groupings there is Sampling Bias.
    * For "168" and "933" groupings there is Sampling Bias.
    * For "168" and "1175" groupings there is Sampling Bias.
    * For "168" and "433" groupings there is Sampling Bias.
    * For "168" and "10501" groupings there is Sampling Bias.
    * For "168" and "7291" groupings there is Sampling Bias.
    * For "168" and "1382" groupings there is Sampling Bias.
    * For "168" and "1067" groupings there is Sampling Bias.
    * For "168" and "5355" groupings there is Sampling Bias.
    * For "168" and "1723" groupings there is Sampling Bias.
    * For "168" and "576" groupings there is Sampling Bias.
    * For "168" and "413" groupings there is Sampling Bias.
    * For "168" and "0" groupings there is Sampling Bias.
    * For "333" and "646" groupings there is Sampling Bias.
    * For "333" and "514" groupings there is Sampling Bias.
    * For "333" and "933" groupings there is Sampling Bias.
    * For "333" and "1175" groupings there is Sampling Bias.
    * For "333" and "433" groupings there is Sampling Bias.
    * For "333" and "10501" groupings there is Sampling Bias.
    * For "333" and "7291" groupings there is Sampling Bias.
    * For "333" and "1382" groupings there is Sampling Bias.
    * For "333" and "1067" groupings there is Sampling Bias.
    * For "333" and "5355" groupings there is Sampling Bias.
    * For "333" and "1723" groupings there is Sampling Bias.
    * For "333" and "576" groupings there is Sampling Bias.
    * For "333" and "413" groupings there is Sampling Bias.
    * For "333" and "0" groupings there is Sampling Bias.
    * For "646" and "514" groupings there is Sampling Bias.
    * For "646" and "933" groupings there is Sampling Bias.
    * For "646" and "1175" groupings there is Sampling Bias.
    * For "646" and "433" groupings there is Sampling Bias.
    * For "646" and "10501" groupings there is Sampling Bias.
    * For "646" and "7291" groupings there is Sampling Bias.
    * For "646" and "1382" groupings there is Sampling Bias.
    * For "646" and "1067" groupings there is Sampling Bias.
    * For "646" and "5355" groupings there is Sampling Bias.
    * For "646" and "1723" groupings there is Sampling Bias.
    * For "646" and "576" groupings there is NO Sampling Bias.
    * For "646" and "413" groupings there is Sampling Bias.
    * For "646" and "0" groupings there is Sampling Bias.
    * For "514" and "933" groupings there is Sampling Bias.
    * For "514" and "1175" groupings there is Sampling Bias.
    * For "514" and "433" groupings there is NO Sampling Bias.
    * For "514" and "10501" groupings there is Sampling Bias.
    * For "514" and "7291" groupings there is Sampling Bias.
    * For "514" and "1382" groupings there is Sampling Bias.
    * For "514" and "1067" groupings there is Sampling Bias.
    * For "514" and "5355" groupings there is Sampling Bias.
    * For "514" and "1723" groupings there is Sampling Bias.
    * For "514" and "576" groupings there is NO Sampling Bias.
    * For "514" and "413" groupings there is Sampling Bias.
    * For "514" and "0" groupings there is Sampling Bias.
    * For "933" and "1175" groupings there is Sampling Bias.
    * For "933" and "433" groupings there is Sampling Bias.
    * For "933" and "10501" groupings there is Sampling Bias.
    * For "933" and "7291" groupings there is Sampling Bias.
    * For "933" and "1382" groupings there is Sampling Bias.
    * For "933" and "1067" groupings there is NO Sampling Bias.
    * For "933" and "5355" groupings there is Sampling Bias.
    * For "933" and "1723" groupings there is Sampling Bias.
    * For "933" and "576" groupings there is Sampling Bias.
    * For "933" and "413" groupings there is Sampling Bias.
    * For "933" and "0" groupings there is Sampling Bias.
    * For "1175" and "433" groupings there is Sampling Bias.
    * For "1175" and "10501" groupings there is Sampling Bias.
    * For "1175" and "7291" groupings there is Sampling Bias.
    * For "1175" and "1382" groupings there is NO Sampling Bias.
    * For "1175" and "1067" groupings there is NO Sampling Bias.
    * For "1175" and "5355" groupings there is Sampling Bias.
    * For "1175" and "1723" groupings there is Sampling Bias.
    * For "1175" and "576" groupings there is Sampling Bias.
    * For "1175" and "413" groupings there is Sampling Bias.
    * For "1175" and "0" groupings there is Sampling Bias.
    * For "433" and "10501" groupings there is Sampling Bias.
    * For "433" and "7291" groupings there is Sampling Bias.
    * For "433" and "1382" groupings there is Sampling Bias.
    * For "433" and "1067" groupings there is Sampling Bias.
    * For "433" and "5355" groupings there is Sampling Bias.
    * For "433" and "1723" groupings there is Sampling Bias.
    * For "433" and "576" groupings there is Sampling Bias.
    * For "433" and "413" groupings there is NO Sampling Bias.
    * For "433" and "0" groupings there is Sampling Bias.
    * For "10501" and "7291" groupings there is Sampling Bias.
    * For "10501" and "1382" groupings there is Sampling Bias.
    * For "10501" and "1067" groupings there is Sampling Bias.
    * For "10501" and "5355" groupings there is Sampling Bias.
    * For "10501" and "1723" groupings there is Sampling Bias.
    * For "10501" and "576" groupings there is Sampling Bias.
    * For "10501" and "413" groupings there is Sampling Bias.
    * For "10501" and "0" groupings there is Sampling Bias.
    * For "7291" and "1382" groupings there is Sampling Bias.
    * For "7291" and "1067" groupings there is Sampling Bias.
    * For "7291" and "5355" groupings there is Sampling Bias.
    * For "7291" and "1723" groupings there is Sampling Bias.
    * For "7291" and "576" groupings there is Sampling Bias.
    * For "7291" and "413" groupings there is Sampling Bias.
    * For "7291" and "0" groupings there is Sampling Bias.
    * For "1382" and "1067" groupings there is Sampling Bias.
    * For "1382" and "5355" groupings there is Sampling Bias.
    * For "1382" and "1723" groupings there is Sampling Bias.
    * For "1382" and "576" groupings there is Sampling Bias.
    * For "1382" and "413" groupings there is Sampling Bias.
    * For "1382" and "0" groupings there is Sampling Bias.
    * For "1067" and "5355" groupings there is Sampling Bias.
    * For "1067" and "1723" groupings there is Sampling Bias.
    * For "1067" and "576" groupings there is Sampling Bias.
    * For "1067" and "413" groupings there is Sampling Bias.
    * For "1067" and "0" groupings there is Sampling Bias.
    * For "5355" and "1723" groupings there is Sampling Bias.
    * For "5355" and "576" groupings there is Sampling Bias.
    * For "5355" and "413" groupings there is Sampling Bias.
    * For "5355" and "0" groupings there is Sampling Bias.
    * For "1723" and "576" groupings there is Sampling Bias.
    * For "1723" and "413" groupings there is Sampling Bias.
    * For "1723" and "0" groupings there is Sampling Bias.
    * For "576" and "413" groupings there is Sampling Bias.
    * For "576" and "0" groupings there is Sampling Bias.
    * For "413" and "0" groupings there is Sampling Bias.
In [74]:
dtc5 = tree.DecisionTreeClassifier(max_depth=5)
dtc5.fit(df_X, df_y)
df_pred = dtc5.predict(df_X)
In [75]:
grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())
In [76]:
print('First subgroup')
print('Targets: ', df_y[selected_feature_grouping[0][0]])
print('Unique targets: ', np.unique(df_y[selected_feature_grouping[0][0]]))
print('Unique predictions: ', np.unique(df_pred[selected_feature_grouping[0][0]]))
print('Confusion matrix: ')
print(grouping_cm[0])

First subgroup
Targets:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
Unique targets:  [0]
Unique predictions:  [0]
Confusion matrix: 
[[51  0]
 [ 0  0]]
In [77]:
for i in range(len(grouping_cm)):
    print(i,' subgroup')
    #print(np.unique(iris_y[selected_feature_grouping[0][1]]))
    #print(np.unique(iris_pred[selected_feature_grouping[0][1]]))
    print('Confusion matrix: ')
    print(grouping_cm[i])

0  subgroup
Confusion matrix: 
[[51  0]
 [ 0  0]]
1  subgroup
Confusion matrix: 
[[162   6]
 [  0   0]]
2  subgroup
Confusion matrix: 
[[317  16]
 [  0   0]]
3  subgroup
Confusion matrix: 
[[606  40]
 [  0   0]]
4  subgroup
Confusion matrix: 
[[487  27]
 [  0   0]]
5  subgroup
Confusion matrix: 
[[871  62]
 [  0   0]]
6  subgroup
Confusion matrix: 
[[1115   60]
 [   0    0]]
7  subgroup
Confusion matrix: 
[[400  33]
 [  0   0]]
8  subgroup
Confusion matrix: 
[[8826 1675]
 [   0    0]]
9  subgroup
Confusion matrix: 
[[5503  784]
 [ 401  603]]
10  subgroup
Confusion matrix: 
[[945 220]
 [ 76 141]]
11  subgroup
Confusion matrix: 
[[732 138]
 [ 70 127]]
12  subgroup
Confusion matrix: 
[[2545  592]
 [ 589 1629]]
13  subgroup
Confusion matrix: 
[[587 226]
 [177 733]]
14  subgroup
Confusion matrix: 
[[ 85  48]
 [ 68 375]]
15  subgroup
Confusion matrix: 
[[ 44  37]
 [ 63 269]]
16  subgroup
Confusion matrix: 
[[0 0]
 [0 0]]
In [78]:
groups_acc=[]
for i in range(len(grouping_cm)):
    group_0_acc = fatf_metrics.accuracy(grouping_cm[i])
    print(group_0_acc)
    groups_acc.append(group_0_acc)

1.0
0.9642857142857143
0.9519519519519519
0.9380804953560371
0.9474708171206225
0.9335476956055734
0.948936170212766
0.9237875288683602
0.8404913817731645
0.8374708544781237
0.7858176555716353
0.8050609184629803
0.7794584500466853
0.7661056297156124
0.7986111111111112
0.7578692493946732
0
In [79]:
fatf_accountability_models.systematic_performance_bias_grid(groups_acc)
Out[79]:
array([[False, False, False, False, False, False, False, False, False,
        False,  True,  True,  True,  True,  True,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False,  True, False,  True,  True,  True,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False,  True, False,  True,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False, False, False,  True,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False,  True, False,  True,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False, False, False, False,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False,  True, False,  True,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False, False, False, False,  True, False,  True,  True],
       [False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False,  True],
       [False, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True,  True, False,  True, False,  True, False, False,
        False, False, False, False, False, False, False,  True],
       [ True, False, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True, False,  True, False, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True, False, False, False, False, False, False, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True, False,
        False, False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True,  True,  True,  True,  True,
         True,  True,  True,  True,  True,  True,  True, False]])
In [80]:
# Select a predictive performance metric
predictive_performance_metric = 'accuracy'
#predictive_performance_metric = 'positive predictive value' # Notice a class index needs to be selected using "label_index" property
#predictive_performance_metric = 'false negative rate' # Notice a class index needs to be selected using "label_index" property

# Select a feature for which the difference in performance should be measured
selected_feature_index = 1 # before was 2, in FAT-forensics example was 1
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5] 

selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
print('Grouping using variable', df_feature_names[selected_feature_index], ' as ', selected_feature_grouping[1])

grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())

print('Resulting confusion matrices:')
for i in range(len(grouping_cm)):
    print('Confusion matrix', i, ':')
    print(grouping_cm[i])

# Compute performance per group in the feature
population_metrics, population_names = fatf_smt.performance_per_subgroup(
    df_X,
    df_y,
    df_pred,
    selected_feature_index,
    groupings=selected_feature_groups,
    metric=predictive_performance_metric, 
    label_index = 2)

#print(population_metrics)

# Print out performance per grouping
print('The *{}* for groups defined on "{}" feature (feature index '
      '{}):'.format(predictive_performance_metric, selected_feature_name,
                    selected_feature_index))
for p_name, p_metric in zip(population_names, population_metrics):
    print('    * For the population split *{}* the {} is: '
          '{:.2f}.'.format(p_name, predictive_performance_metric, p_metric))

# Evaluate Systematic Performance Bias
bias_grid = fatf_accountability_models.systematic_performance_bias_grid(population_metrics)
print('Systematic performance grid check:')
print(bias_grid)

# Print out Systematic Performance Bias for each grouping pair
print('\nThe *{}-based* Systematic Performance Bias for *{}* feature (index '
      '{}) grouping is:'.format(predictive_performance_metric,
                                selected_feature_name, selected_feature_index))
                                
for grouping_i, grouping_name_i in enumerate(population_names):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(population_names[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Systematic '
              'Performance Bias.'.format(grouping_name_i, grouping_name_j,
                                         is_not))

Grouping using variable education-num  as  ['x <= 1.5', '1.5 < x <= 2.5', '2.5 < x <= 3.5', '3.5 < x <= 4.5', '4.5 < x <= 5.5', '5.5 < x <= 6.5', '6.5 < x <= 7.5', '7.5 < x <= 8.5', '8.5 < x <= 9.5', '9.5 < x <= 10.5', '10.5 < x <= 11.5', '11.5 < x <= 12.5', '12.5 < x <= 13.5', '13.5 < x <= 14.5', '14.5 < x <= 15.5', '15.5 < x <= 16.5', '16.5 < x']
Resulting confusion matrices:
Confusion matrix 0 :
[[51  0]
 [ 0  0]]
Confusion matrix 1 :
[[162   6]
 [  0   0]]
Confusion matrix 2 :
[[317  16]
 [  0   0]]
Confusion matrix 3 :
[[606  40]
 [  0   0]]
Confusion matrix 4 :
[[487  27]
 [  0   0]]
Confusion matrix 5 :
[[871  62]
 [  0   0]]
Confusion matrix 6 :
[[1115   60]
 [   0    0]]
Confusion matrix 7 :
[[400  33]
 [  0   0]]
Confusion matrix 8 :
[[8826 1675]
 [   0    0]]
Confusion matrix 9 :
[[5503  784]
 [ 401  603]]
Confusion matrix 10 :
[[945 220]
 [ 76 141]]
Confusion matrix 11 :
[[732 138]
 [ 70 127]]
Confusion matrix 12 :
[[2545  592]
 [ 589 1629]]
Confusion matrix 13 :
[[587 226]
 [177 733]]
Confusion matrix 14 :
[[ 85  48]
 [ 68 375]]
Confusion matrix 15 :
[[ 44  37]
 [ 63 269]]
Confusion matrix 16 :
[[0 0]
 [0 0]]
The *accuracy* for groups defined on "education-num" feature (feature index 1):
    * For the population split *x <= 1.5* the accuracy is: 1.00.
    * For the population split *1.5 < x <= 2.5* the accuracy is: 0.96.
    * For the population split *2.5 < x <= 3.5* the accuracy is: 0.95.
    * For the population split *3.5 < x <= 4.5* the accuracy is: 0.94.
    * For the population split *4.5 < x <= 5.5* the accuracy is: 0.95.
    * For the population split *5.5 < x <= 6.5* the accuracy is: 0.93.
    * For the population split *6.5 < x <= 7.5* the accuracy is: 0.95.
    * For the population split *7.5 < x <= 8.5* the accuracy is: 0.92.
    * For the population split *8.5 < x <= 9.5* the accuracy is: 0.84.
    * For the population split *9.5 < x <= 10.5* the accuracy is: 0.84.
    * For the population split *10.5 < x <= 11.5* the accuracy is: 0.79.
    * For the population split *11.5 < x <= 12.5* the accuracy is: 0.81.
    * For the population split *12.5 < x <= 13.5* the accuracy is: 0.78.
    * For the population split *13.5 < x <= 14.5* the accuracy is: 0.77.
    * For the population split *14.5 < x <= 15.5* the accuracy is: 0.80.
    * For the population split *15.5 < x <= 16.5* the accuracy is: 0.76.
    * For the population split *16.5 < x* the accuracy is: 0.00.
Systematic performance grid check:
[[False False False False False False False False False False  True  True
   True  True  True  True  True]
 [False False False False False False False False False False  True False
   True  True  True  True  True]
 [False False False False False False False False False False  True False
   True  True False  True  True]
 [False False False False False False False False False False False False
   True  True False  True  True]
 [False False False False False False False False False False  True False
   True  True False  True  True]
 [False False False False False False False False False False False False
  False  True False  True  True]
 [False False False False False False False False False False  True False
   True  True False  True  True]
 [False False False False False False False False False False False False
  False  True False  True  True]
 [False False False False False False False False False False False False
  False False False False  True]
 [False False False False False False False False False False False False
  False False False False  True]
 [ True  True  True False  True False  True False False False False False
  False False False False  True]
 [ True False False False False False False False False False False False
  False False False False  True]
 [ True  True  True  True  True False  True False False False False False
  False False False False  True]
 [ True  True  True  True  True  True  True  True False False False False
  False False False False  True]
 [ True  True False False False False False False False False False False
  False False False False  True]
 [ True  True  True  True  True  True  True  True False False False False
  False False False False  True]
 [ True  True  True  True  True  True  True  True  True  True  True  True
   True  True  True  True False]]

The *accuracy-based* Systematic Performance Bias for *education-num* feature (index 1) grouping is:
    * For "x <= 1.5" and "1.5 < x <= 2.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "2.5 < x <= 3.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "3.5 < x <= 4.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "4.5 < x <= 5.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "5.5 < x <= 6.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "x <= 1.5" and "10.5 < x <= 11.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "11.5 < x <= 12.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "14.5 < x <= 15.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "x <= 1.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "2.5 < x <= 3.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "3.5 < x <= 4.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "4.5 < x <= 5.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "5.5 < x <= 6.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "10.5 < x <= 11.5" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "14.5 < x <= 15.5" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "1.5 < x <= 2.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "3.5 < x <= 4.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "4.5 < x <= 5.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "5.5 < x <= 6.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "10.5 < x <= 11.5" groupings there is Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "2.5 < x <= 3.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "4.5 < x <= 5.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "5.5 < x <= 6.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "10.5 < x <= 11.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "3.5 < x <= 4.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "5.5 < x <= 6.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "10.5 < x <= 11.5" groupings there is Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "4.5 < x <= 5.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "6.5 < x <= 7.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "10.5 < x <= 11.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "5.5 < x <= 6.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "7.5 < x <= 8.5" groupings there is NO Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "10.5 < x <= 11.5" groupings there is Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "12.5 < x <= 13.5" groupings there is Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "6.5 < x <= 7.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "8.5 < x <= 9.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "10.5 < x <= 11.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "13.5 < x <= 14.5" groupings there is Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "15.5 < x <= 16.5" groupings there is Systematic Performance Bias.
    * For "7.5 < x <= 8.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "9.5 < x <= 10.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "10.5 < x <= 11.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "13.5 < x <= 14.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "8.5 < x <= 9.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "10.5 < x <= 11.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "13.5 < x <= 14.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "9.5 < x <= 10.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "11.5 < x <= 12.5" groupings there is NO Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "13.5 < x <= 14.5" groupings there is NO Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "10.5 < x <= 11.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "11.5 < x <= 12.5" and "12.5 < x <= 13.5" groupings there is NO Systematic Performance Bias.
    * For "11.5 < x <= 12.5" and "13.5 < x <= 14.5" groupings there is NO Systematic Performance Bias.
    * For "11.5 < x <= 12.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "11.5 < x <= 12.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "11.5 < x <= 12.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "12.5 < x <= 13.5" and "13.5 < x <= 14.5" groupings there is NO Systematic Performance Bias.
    * For "12.5 < x <= 13.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "12.5 < x <= 13.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "12.5 < x <= 13.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "13.5 < x <= 14.5" and "14.5 < x <= 15.5" groupings there is NO Systematic Performance Bias.
    * For "13.5 < x <= 14.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "13.5 < x <= 14.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "14.5 < x <= 15.5" and "15.5 < x <= 16.5" groupings there is NO Systematic Performance Bias.
    * For "14.5 < x <= 15.5" and "16.5 < x" groupings there is Systematic Performance Bias.
    * For "15.5 < x <= 16.5" and "16.5 < x" groupings there is Systematic Performance Bias.

Sampling bias and systematic performance bias with sex¶

In [81]:
print(df['sex_encoded'].max())
print(df['sex_encoded'].min())
print(df['sex_encoded'].unique())

1
0
[1 0]
In [82]:
# Select a feature for which the Sampling Bias be measured
selected_feature_index = 9
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [0.5]
In [83]:
selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
selected_feature_grouping[1]
Out[83]:
['x <= 0.5', '0.5 < x']
In [84]:
print(len(selected_feature_grouping[0][0]))
print(len(selected_feature_grouping[0][1]))

10771
21790
In [85]:
counts_per_grouping = [len(i) for i in selected_feature_grouping[0]]
print(counts_per_grouping)
print(counts_per_grouping)
fatf_accountability_data.sampling_bias_grid_check(counts_per_grouping)

[10771, 21790]
[10771, 21790]
Out[85]:
array([[False,  True],
       [ True, False]])
In [86]:
# Get the disparity grid
bias_grid = fatf_dam.sampling_bias_grid_check(counts_per_grouping)
print(bias_grid)

# Print out disparity per every grouping pair
print('\nThe Sampling Bias for *{}* feature (index {}) grouping is:'
      ''.format(selected_feature_name, selected_feature_index))
for grouping_i, grouping_name_i in enumerate(counts_per_grouping):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(counts_per_grouping[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Sampling Bias.'
              ''.format(grouping_name_i, grouping_name_j, is_not))

[[False  True]
 [ True False]]

The Sampling Bias for *sex_encoded* feature (index 9) grouping is:
    * For "10771" and "21790" groupings there is Sampling Bias.
In [87]:
dtc5 = tree.DecisionTreeClassifier(max_depth=5)
dtc5.fit(df_X, df_y)
df_pred = dtc5.predict(df_X)
In [88]:
grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())
In [89]:
print('First subgroup')
print('Targets: ', df_y[selected_feature_grouping[0][0]])
print('Unique targets: ', np.unique(df_y[selected_feature_grouping[0][0]]))
print('Unique predictions: ', np.unique(df_pred[selected_feature_grouping[0][0]]))
print('Confusion matrix: ')
print(grouping_cm[0])

First subgroup
Targets:  [0 0 0 ... 0 0 1]
Unique targets:  [0 1]
Unique predictions:  [0 1]
Confusion matrix: 
[[9462  764]
 [ 130  415]]
In [90]:
print('Second subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][1]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][1]]))
print('Confusion matrix: ')
print(grouping_cm[1])

Second subgroup
Confusion matrix: 
[[13814  3200]
 [ 1314  3462]]
In [91]:
group_0_acc = fatf_metrics.accuracy(grouping_cm[0])
print(group_0_acc)
group_1_acc = fatf_metrics.accuracy(grouping_cm[1])
print(group_1_acc)

0.9169993501067681
0.7928407526388251
In [92]:
fatf_accountability_models.systematic_performance_bias_grid([group_0_acc, group_1_acc])
Out[92]:
array([[False, False],
       [False, False]])
In [93]:
# Select a predictive performance metric
predictive_performance_metric = 'accuracy'
#predictive_performance_metric = 'positive predictive value' # Notice a class index needs to be selected using "label_index" property
#predictive_performance_metric = 'false negative rate' # Notice a class index needs to be selected using "label_index" property

# Select a feature for which the difference in performance should be measured
selected_feature_index = 9 # before was 2, in FAT-forensics example was 1
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [0.5] 

selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
print('Grouping using variable', df_feature_names[selected_feature_index], ' as ', selected_feature_grouping[1])

grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())

print('Resulting confusion matrices:')
for i in range(len(grouping_cm)):
    print('Confusion matrix', i, ':')
    print(grouping_cm[i])

# Compute performance per group in the feature
population_metrics, population_names = fatf_smt.performance_per_subgroup(
    df_X,
    df_y,
    df_pred,
    selected_feature_index,
    groupings=selected_feature_groups,
    metric=predictive_performance_metric, 
    label_index = 2)

#print(population_metrics)

# Print out performance per grouping
print('The *{}* for groups defined on "{}" feature (feature index '
      '{}):'.format(predictive_performance_metric, selected_feature_name,
                    selected_feature_index))
for p_name, p_metric in zip(population_names, population_metrics):
    print('    * For the population split *{}* the {} is: '
          '{:.2f}.'.format(p_name, predictive_performance_metric, p_metric))

# Evaluate Systematic Performance Bias
bias_grid = fatf_accountability_models.systematic_performance_bias_grid(population_metrics)
print('Systematic performance grid check:')
print(bias_grid)

# Print out Systematic Performance Bias for each grouping pair
print('\nThe *{}-based* Systematic Performance Bias for *{}* feature (index '
      '{}) grouping is:'.format(predictive_performance_metric,
                                selected_feature_name, selected_feature_index))
                                
for grouping_i, grouping_name_i in enumerate(population_names):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(population_names[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Systematic '
              'Performance Bias.'.format(grouping_name_i, grouping_name_j,
                                         is_not))

Grouping using variable sex_encoded  as  ['x <= 0.5', '0.5 < x']
Resulting confusion matrices:
Confusion matrix 0 :
[[9462  764]
 [ 130  415]]
Confusion matrix 1 :
[[13814  3200]
 [ 1314  3462]]
The *accuracy* for groups defined on "sex_encoded" feature (feature index 9):
    * For the population split *x <= 0.5* the accuracy is: 0.92.
    * For the population split *0.5 < x* the accuracy is: 0.79.
Systematic performance grid check:
[[False False]
 [False False]]

The *accuracy-based* Systematic Performance Bias for *sex_encoded* feature (index 9) grouping is:
    * For "x <= 0.5" and "0.5 < x" groupings there is NO Systematic Performance Bias.

Sampling bias and systematic performance bias with age¶

In [94]:
print(df['age'].max())
print(df['age'].min())
print(df['age'].unique())

90
17
[39 50 38 53 28 37 49 52 31 42 30 23 32 40 34 25 43 54 35 59 56 19 20 45
 22 48 21 24 57 44 41 29 18 47 46 36 79 27 67 33 76 17 55 61 70 64 71 68
 66 51 58 26 60 90 75 65 77 62 63 80 72 74 69 73 81 78 88 82 83 84 85 86
 87]
In [95]:
# Select a feature for which the Sampling Bias be measured
selected_feature_index = 0
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [25,45,75,90]
In [96]:
selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
selected_feature_grouping[1]
Out[96]:
['x <= 25', '25 < x <= 45', '45 < x <= 75', '75 < x <= 90', '90 < x']
In [97]:
print(len(selected_feature_grouping[0][0]))
print(len(selected_feature_grouping[0][1]))
print(len(selected_feature_grouping[0][2]))
print(len(selected_feature_grouping[0][3]))

6411
16523
9386
241
In [98]:
counts_per_grouping = [len(i) for i in selected_feature_grouping[0]]
print(counts_per_grouping)
print(counts_per_grouping)
fatf_accountability_data.sampling_bias_grid_check(counts_per_grouping)

[6411, 16523, 9386, 241, 0]
[6411, 16523, 9386, 241, 0]
Out[98]:
array([[False,  True,  True,  True,  True],
       [ True, False,  True,  True,  True],
       [ True,  True, False,  True,  True],
       [ True,  True,  True, False,  True],
       [ True,  True,  True,  True, False]])
In [99]:
# Get the disparity grid
bias_grid = fatf_dam.sampling_bias_grid_check(counts_per_grouping)
print(bias_grid)

# Print out disparity per every grouping pair
print('\nThe Sampling Bias for *{}* feature (index {}) grouping is:'
      ''.format(selected_feature_name, selected_feature_index))
for grouping_i, grouping_name_i in enumerate(counts_per_grouping):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(counts_per_grouping[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Sampling Bias.'
              ''.format(grouping_name_i, grouping_name_j, is_not))

[[False  True  True  True  True]
 [ True False  True  True  True]
 [ True  True False  True  True]
 [ True  True  True False  True]
 [ True  True  True  True False]]

The Sampling Bias for *age* feature (index 0) grouping is:
    * For "6411" and "16523" groupings there is Sampling Bias.
    * For "6411" and "9386" groupings there is Sampling Bias.
    * For "6411" and "241" groupings there is Sampling Bias.
    * For "6411" and "0" groupings there is Sampling Bias.
    * For "16523" and "9386" groupings there is Sampling Bias.
    * For "16523" and "241" groupings there is Sampling Bias.
    * For "16523" and "0" groupings there is Sampling Bias.
    * For "9386" and "241" groupings there is Sampling Bias.
    * For "9386" and "0" groupings there is Sampling Bias.
    * For "241" and "0" groupings there is Sampling Bias.
In [100]:
dtc5 = tree.DecisionTreeClassifier(max_depth=5)
dtc5.fit(df_X, df_y)
df_pred = dtc5.predict(df_X)
In [101]:
grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())
In [102]:
print('First subgroup')
print('Targets: ', df_y[selected_feature_grouping[0][0]])
print('Unique targets: ', np.unique(df_y[selected_feature_grouping[0][0]]))
print('Unique predictions: ', np.unique(df_pred[selected_feature_grouping[0][0]]))
print('Confusion matrix: ')
print(grouping_cm[0])

First subgroup
Targets:  [0 0 0 ... 0 0 0]
Unique targets:  [0 1]
Unique predictions:  [0 1]
Confusion matrix: 
[[6293  112]
 [   4    2]]
In [103]:
print('Second subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][1]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][1]]))
print('Confusion matrix: ')
print(grouping_cm[1])

print('Third subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][2]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][2]]))
print('Confusion matrix: ')
print(grouping_cm[2])

print('Fourth subgroup')
#print(np.unique(iris_y[selected_feature_grouping[0][3]]))
#print(np.unique(iris_pred[selected_feature_grouping[0][3]]))
print('Confusion matrix: ')
print(grouping_cm[3])

Second subgroup
Confusion matrix: 
[[11306  2179]
 [  852  2186]]
Third subgroup
Confusion matrix: 
[[5491 1650]
 [ 573 1672]]
Fourth subgroup
Confusion matrix: 
[[186  23]
 [ 15  17]]
In [104]:
group_0_acc = fatf_metrics.accuracy(grouping_cm[0])
print(group_0_acc)
group_1_acc = fatf_metrics.accuracy(grouping_cm[1])
print(group_1_acc)
group_2_acc = fatf_metrics.accuracy(grouping_cm[2])
print(group_2_acc)
group_3_acc = fatf_metrics.accuracy(grouping_cm[3])
print(group_3_acc)

0.9819060988925284
0.8165587363069661
0.7631578947368421
0.8423236514522822
In [105]:
fatf_accountability_models.systematic_performance_bias_grid([group_0_acc, group_1_acc,group_2_acc,group_3_acc])
Out[105]:
array([[False,  True,  True, False],
       [ True, False, False, False],
       [ True, False, False, False],
       [False, False, False, False]])
In [106]:
# Select a predictive performance metric
predictive_performance_metric = 'accuracy'
#predictive_performance_metric = 'positive predictive value' # Notice a class index needs to be selected using "label_index" property
#predictive_performance_metric = 'false negative rate' # Notice a class index needs to be selected using "label_index" property

# Select a feature for which the difference in performance should be measured
selected_feature_index = 0 # before was 2, in FAT-forensics example was 1
selected_feature_name = df_feature_names[selected_feature_index]

# Define grouping on the selected feature
selected_feature_groups = [25,45,75,90] 

selected_feature_grouping = fatf_data_tools.group_by_column(
    df_X,
    selected_feature_index,
    groupings=selected_feature_groups)
print('Grouping using variable', df_feature_names[selected_feature_index], ' as ', selected_feature_grouping[1])

grouping_cm = fatf_metrics_tools.confusion_matrix_per_subgroup_indexed(
    selected_feature_grouping[0],
    df_y,
    df_pred,
    labels=np.unique(df_y).tolist())

print('Resulting confusion matrices:')
for i in range(len(grouping_cm)):
    print('Confusion matrix', i, ':')
    print(grouping_cm[i])

# Compute performance per group in the feature
population_metrics, population_names = fatf_smt.performance_per_subgroup(
    df_X,
    df_y,
    df_pred,
    selected_feature_index,
    groupings=selected_feature_groups,
    metric=predictive_performance_metric, 
    label_index = 2)

#print(population_metrics)

# Print out performance per grouping
print('The *{}* for groups defined on "{}" feature (feature index '
      '{}):'.format(predictive_performance_metric, selected_feature_name,
                    selected_feature_index))
for p_name, p_metric in zip(population_names, population_metrics):
    print('    * For the population split *{}* the {} is: '
          '{:.2f}.'.format(p_name, predictive_performance_metric, p_metric))

# Evaluate Systematic Performance Bias
bias_grid = fatf_accountability_models.systematic_performance_bias_grid(population_metrics)
print('Systematic performance grid check:')
print(bias_grid)

# Print out Systematic Performance Bias for each grouping pair
print('\nThe *{}-based* Systematic Performance Bias for *{}* feature (index '
      '{}) grouping is:'.format(predictive_performance_metric,
                                selected_feature_name, selected_feature_index))
                                
for grouping_i, grouping_name_i in enumerate(population_names):
    j_offset = grouping_i + 1
    for grouping_j, grouping_name_j in enumerate(population_names[j_offset:]):
        grouping_j += j_offset
        is_not = '' if bias_grid[grouping_i, grouping_j] else ' NO'

        print('    * For "{}" and "{}" groupings there is{} Systematic '
              'Performance Bias.'.format(grouping_name_i, grouping_name_j,
                                         is_not))

Grouping using variable age  as  ['x <= 25', '25 < x <= 45', '45 < x <= 75', '75 < x <= 90', '90 < x']
Resulting confusion matrices:
Confusion matrix 0 :
[[6293  112]
 [   4    2]]
Confusion matrix 1 :
[[11306  2179]
 [  852  2186]]
Confusion matrix 2 :
[[5491 1650]
 [ 573 1672]]
Confusion matrix 3 :
[[186  23]
 [ 15  17]]
Confusion matrix 4 :
[[0 0]
 [0 0]]
The *accuracy* for groups defined on "age" feature (feature index 0):
    * For the population split *x <= 25* the accuracy is: 0.98.
    * For the population split *25 < x <= 45* the accuracy is: 0.82.
    * For the population split *45 < x <= 75* the accuracy is: 0.76.
    * For the population split *75 < x <= 90* the accuracy is: 0.84.
    * For the population split *90 < x* the accuracy is: 0.00.
Systematic performance grid check:
[[False  True  True False  True]
 [ True False False False  True]
 [ True False False False  True]
 [False False False False  True]
 [ True  True  True  True False]]

The *accuracy-based* Systematic Performance Bias for *age* feature (index 0) grouping is:
    * For "x <= 25" and "25 < x <= 45" groupings there is Systematic Performance Bias.
    * For "x <= 25" and "45 < x <= 75" groupings there is Systematic Performance Bias.
    * For "x <= 25" and "75 < x <= 90" groupings there is NO Systematic Performance Bias.
    * For "x <= 25" and "90 < x" groupings there is Systematic Performance Bias.
    * For "25 < x <= 45" and "45 < x <= 75" groupings there is NO Systematic Performance Bias.
    * For "25 < x <= 45" and "75 < x <= 90" groupings there is NO Systematic Performance Bias.
    * For "25 < x <= 45" and "90 < x" groupings there is Systematic Performance Bias.
    * For "45 < x <= 75" and "75 < x <= 90" groupings there is NO Systematic Performance Bias.
    * For "45 < x <= 75" and "90 < x" groupings there is Systematic Performance Bias.
    * For "75 < x <= 90" and "90 < x" groupings there is Systematic Performance Bias.
In [ ]: